Remote experimentation through Arduino-based Remote Laboratories

The challenges and solutions for inclusive engineering education are discussed in this paper. We propose remote experimentation as the practical-oriented basis to train engineers with disabilities in the fields of Computer Science and Information Technologies. The structure and the functionality of international GOLDi network that unites partner universities from Germany, Australia, Ukraine, Armenia and Georgia is given. The possibilities of REIoT complex for studying the features of embedded systems design and Internet of Things technologies as well as an overview of ISRT laboratory for embedded software development and testing are given. The presented Remote Laboratories are successfully used to improve educational services quality and accessibility as well as to strengthen the practical component of the learning process.

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A survey on remote laboratories for e-learning and remote experimentation

Contemporary Engineering Sciences ◽

10.12988/ces.2014.411218 ◽

2014 ◽

Vol 7 ◽

pp. 1617-1624 ◽

Cited By ~ 2

Author(s):

Abdallah Mokhtar ◽

Ghali R Mikhail ◽

Choi Seong-Joo

Keyword(s):

Remote Laboratories ◽

E Learning ◽

Remote Experimentation

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Implementation of a Pilot Plant for the Integral Development of Remote Laboratories

IEEE Revista Iberoamericana de Tecnologias del Aprendizaje ◽

10.1109/rita.2021.3052491 ◽

2021 ◽

Vol 16 (1) ◽

pp. 11-20

Author(s):

Juan Valdiviezo Espinoza ◽

William Ipanaque Alama ◽

Juan Soto Bohorquez ◽

Ivan Belupu Amaya

Keyword(s):

Pilot Plant ◽

Remote Laboratories ◽

Integral Development

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Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

ACM/IMS Transactions on Data Science ◽

10.1145/3437260 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-13

Author(s):

Seid Miad Zandavi ◽

Vera Chung ◽

Ali Anaissi

Keyword(s):

Genetic Algorithm ◽

Optimization Algorithm ◽

Heuristic Algorithms ◽

Simplex Algorithm ◽

Local Optimum ◽

Hybrid Optimization Algorithm ◽

Multimodal Function ◽

Remote Laboratories ◽

Remote Lab ◽

Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

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